Sleep spindles are a defining characteristic of Stage 2 non-rapid eye movement sleep (N2), evident in the electroencephalogram as brief powerful bursts of 12-15Hz synchronous activity. They are generated in the thalamic reticular nucleus and are synchronized by thalamocortical circuitry. Spindles are a mechanism of synaptic plasticity and are strongly linked to memory consolidation and IQ. Spindles are abnormal in patients with neuropsychiatric and neurodevelopmental illnesses (including schizophrenia, autism, major depression and intellectual disability) as well as in the first-degree relatives of individuals with schizophrenia. This suggests that sleep spindles are a potential endophenotype for neuropsychiatric and neurodevelopmental disorders characterized by cognitive impairments. Importantly, sleep spindles can be pharmacologically manipulated, and increasing spindles improves memory consolidation in healthy individuals, suggesting that spindles are a novel therapeutic target. Identifying genetic contributions to spindle activity will therefore illuminate their mechanisms and potentially guide the development of treatments. Although spindles are highly heritable, very little is known about the specific genes that drive variation in their expression. To address this, we first propose to exploit existing large-scale datasets to elucidate the genetic architecture of sleep spindles as well as other heritable sleep parameters. Second, we will examine the effects of known genetic risk factors for neuropsychiatric and neurodevelopmental disorders on spindle and other sleep parameters. We will leverage single nucleotide polymorphism (SNP) data from genome-wide association studies (GWAS) previously performed on large sleep study cohorts, via the National Sleep Research Resource (NSRR), which contains electroencephalography (EEG), and genetic data on thousands of individuals. Most NSRR studies were designed to examine medical sleep disorders such as obstructive sleep apnea and so these data represent a valuable resource not yet mined for cognitive and neuropsychiatric genetic research. Specifically, we will develop a computational pipeline to process the thousands of whole-night EEG recordings, detect spindles and report on other aspects of sleep architecture, including spectral power. We will perform a genome-wide association study for these phenotypes and use state-of-the-art analysis methods to characterize the genetic architecture of these sleep parameters, with a focus on shared heritability and the extent to which risk genes for psychiatric diseases are also implicated in individual differences in brain activity during sleep. By using a genetic paradigm, this research can address the mechanistic and causal relations between sleep, cognition and disease.